AI智能体冲击零信任安全架构：贝尔法斯特女王大学实测去认证攻击仅需2秒

This article is brought to you by Queen’s University Belfast . Several teams around the globe are leveraging AI by creating a task force of agents managed by an orchestrator and creating applications to support risk assessment, client profiling, match making, and information yield with a slight overview of decision making. One of the important components for these agents is to ensure that the logs are managed and historic relevance is not being used to avoid skewing towards same decision making. Thus, the system is expected to keep checkpoints in place where agents are prompted to delete such historic relevance. However, despite such methodology in place, a future prompt could make the agents bring forward the information that was supposed to be deleted and can be tricked into revealing more information, which is a classic case of context recovery attack. So, the question raised here is: How can we trust that our instructions have been followed? And where is the proof? There is an element of utilizing zero-trust architectures (ZTA) for orchestrating and managing agentic activity, which simply means all agentic interactions use the core principle of “never trust,” “always verify.” ZTA components include a policy enforcement point (PEP) that has to be trusted to maintain trust across the system, so the question that remains is where is the component of zero-trust, and shall we trust the policies enforced by PEP? Having a pro-agentic approach and utilizing agents could be helpful with the right guardrails in place. Opening our systems to random agents that are being trained on off-the-shelf models and blindly relying on them can pose significant cyber risks. For example, vibe coding by using an AI model to help develop software, giving away the entire codebase to an untrusted entity outside the organization can lead to vulnerability sweeping across multiple surfaces. Vibe coding by using an AI model to help develop software, giving away the entire codebase to an untrusted entity outside the organization can lead to vulnerability sweeping across multiple surfaces. This opens critical discussions towards how vibe coding could interfere with the positioning and implementation of trusted execution of the agentic queries. Consider an autonomous web of agents that relies on agents’ inputs to drive the operations. When the system is operating under low tolerance of failure and with non-tracking based agentic workflows, it could be computationally expensive to trust and verify each query in real-time that leads to the common trade-offs of memory vs execution. Another risk to consider is the zero-day attack. When it happens, how will the system react? Would such an event be manageable without a total infrastructure breakdown? The scale of impact can be observed from over reliance on agentic ecosystems with only one trusted layer. This could mean that when failure occurs, the entire ecosystem could be exposed, just like there will be no autonomy if the backbone network is not available, which essentially means no layer to place trust. How quickly can de-authentication attacks occur? An in-house experiment done at Queen’s University Belfast (QUB) provided an initial understanding of how the scale of agentic attacks could mean a lower window of vulnerability. The team focused on very popular de-authentication attacks and with agentic orchestration, the swift rate to do de-authentication attacks could go as low as 2 to 3 seconds. This gives little to no time to react and decide on patching the system. There is another aspect of the large-scale agentic system, which establishes trust under asynchronous autonomy. Trust serves as a critical foundation for traceability. How would you ensure that all the agents operating towards a common goal are aligned and understand the delays associated with their goals? Our team worked around this space, which at first involved demonstrating that the problem exists, and its impact and scale can be huge when you must prove trust under boundaries of asynchronous autonomy. Solutions towards such problems can be simplified by keeping things like AI-bill of material or leveraging distributed ledger technology that can provide underlying infrastructure of trust via traceability and immutability and keep the agentic activity and associated trust vectors agnostic to the platform. Within trust, another factor that is not well explored but needs a thorough investigation is the concept of horizontal anomalies. It defines how agents interact differently with various sources under different settings. As agents behave correctly and align themselves with the architectural expectations for one specific task or application, that does not necessarily mean these can be trusted for their operations across all associated components. How to identify such anomalies and what methods can be employed without complying with such agents remain open questions and topics for further exploration. The critical aspect is